Systems | Development | Analytics | API | Testing

Your AI coding agent just made every test pass. Ship it, right? Not so fast. A growing class of AI-generated bugs doesn’t come from writing bad code. It comes from the AI changing working code to accommodate its own mistakes. This isn’t a theoretical risk. It’s happening now, in production codebases, and it’s harder to catch than any bug the AI might introduce from scratch.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

Service virtualization tools have become indispensable for organizations seeking to streamline their testing and development processes. These tools allow teams to simulate the behavior of critical software components, enabling more rapid development with overall cost reduction and improved collaborative outcomes. As demand mounts for service virtualization solutions, identifying the best tools to support this workflow in the software development lifecycle has never been so important.

For teams using CI/CD, the specs of the build machine can have a significant impact on development productivity. Faster builds mean shorter fix-and-verify cycles, which speed up the overall development process. However, it’s hard to know how fast each CI/CD service actually is without comparing them under the same conditions. In this article, I compare the iOS build speeds of GitHub Actions, Bitrise, and Codemagic using the same Flutter project, and compare them in terms of cost-performance as well.

Most teams already know a lot about production. They have dashboards. They have traces. They have alerts. They have enough telemetry to explain what happened after an incident and enough graphs to argue about it for the rest of the week. Then they go to test a change and start from scratch. The integration tests hit a hand-written mock that returns {"status": "ok"}. The load tests replay a CSV somebody exported months ago. Staging is close enough to production right up until it matters.